Bingyi Kang

Evidential cognitive maps

In order to handle uncertain information, this paper proposes evidential cognitive maps (ECMs), similar to the fuzzy cognitive maps (FCMs). ECMs are uncertain-graph structures for representing causal reasoning through the combination of cognitive maps and Dempster Shafer evidence theory. The framework of ECMs is developed in detail and an application to socio-economic model is used to illustrate the application of the proposed methodology.

Stable strategies analysis based on the utility of Z-number in the evolutionary games

Evolutionary games with the fuzzy set are attracting growing interest. While among previous studies, the role of the reliability of knowledge in such an infrastructure is still virgin and may become a fascinating issue. Z-number is combined with “restriction” and “reliability”, which is an efficient framework to simulate the thinking of human. In this paper, the stable strategies analysis based on the utility of Z-number in the evolutionary games is proposed, which can simulate the procedure of human’s competition and cooperation more authentically and more flexibly. Some numerical examples and an application are used to illustrate the effectiveness of the proposed methodology. Results show that total utility of Z-number can be used as an index to extend the classical evolutionary games into ones linguistic-based, which is applicable in the real applications since the payoff matrix is always determined by the knowledge of human using uncertain information, e.g., (outcome of the next year, about fifty thousand dollars, likely).

Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

Conflict management based on belief function entropy in sensor fusion

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster–Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.

Generating Z-number based on OWA weights using maximum entropy

In the application of Z‐number, how to generate Z‐number is a significant and open issue. In this paper, we proposed a method of generating Z‐number based on the OWA weights using maximum entropy considering the attitude (preference) of the decision maker. Some numerical examples are used to illustrate the effectiveness of the proposed method. Results show that the attitude (preference) of the decision maker can give an optimal possibility distribution of the reliability for Z‐number using maximum entropy.

Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory

Clostridium difficile infection is one of the major patient safety concerns in hospitals worldwide. Clostridium difficile infection can have high economic burden to patients, hospitals, and government. Limited work has been done in the area of predictive modeling. In this article, A new predictive model based on Gaussian mixture model and Dempster–Shafter theory is proposed to predict Clostridium difficile infection incidence in hospitals. First, the Gaussian mixture model and expectation–maximization algorithms are used to generate explicit probability criteria of risk factors based on the given data. Second, Dempster–Shafter theory is used to predict the Clostridium difficile infection incidence based on the generated probability criteria that have different beliefs attributing to their different credits. The main procedure includes (1) generate the probability criteria model using Gaussian mixture model and expectation–maximization algorithm; (2) determine the credit of the probability criteria; (3) generate the basic probability assignment; (4) discount the evidences; (5) aggregate the evidences using Dempster combining rule; (6) predict Clostridium difficile infection incidence using pignistic probability transformation. Results show that the model has a higher accuracy than an existing model. The proposed model can generate the criteria ratings of risk factors automatically, which would potentially prevent the imprecision caused by the subjective judgement of experts. The proposed model can assist risk managers and hospital administrators in the prediction and control of Clostridium difficile infection incidence with optimizing their resources.

An application of genetic algorithm for university course timetabling problem

Timetabling problems are a process of assigning a given set of events and resources to the limited space and time under hard constraints which are rigidly enforced and soft constraints which are satisfied as nearly as possible. As a kind of timetabling problems, university course timetabling is a very important administrative activity for a wide variety of schools. Genetic algorithm is an advanced heuristic method which is very effective in many fields. In this paper, genetic algorithm is used to solve university course timetabling problem. At first, a model of problem to be solved is defined. Then, the genetic representation is determined and a fitness function is established according to the constraints. Finally, a case of university course timetabling from real-world is discussed and solved. It is demonstrated that the method proposed in this paper is feasible and efficient.

Total utility of Z-number

Z-numbers, combined with “constraint” and “reliability”, has more power to express human knowledge. How to determine the ordering of Z-numbers and how to make a decision with Z-numbers are both meaningful and open issues. In this paper, a new notion of the total utility of Z-number is proposed to measure the total effects of a Z-number. The proposed total utility of Z-number can be used to determine the ordering of Z-numbers, and can also be simply applied in the application of multi-criteria decision making under uncertain environments. Two particular cases of Z-number (Gaussian and triangular), and some mathematical properties of the total utility of Z-number are discussed in this paper. Several applications and comparative analyses are shown to demonstrate the effectiveness of the proposed total utility of Z-number in the application of ordering Z-numbers and multi-criteria decision making.

Generalized fuzzy cognitive maps: a new extension of fuzzy cognitive maps

A fuzzy cognitive maps (FCM) is a cognitive map within the relations between the elements. FCM has been widely used in many applications such as experts system and knowledge engineering. However, classical FCM is inherently short of sufficient capability of representing and aggregating uncertain information. In this paper, generalized FCM (GFCM) is proposed based on genetic algorithm and interval numbers. An application frame of GFCM is detailed. At last, a numerical example about socio-economic system is used to illustrate the effectiveness of the proposed methodology.

The selection of the Dempster's rule based on evidence trap problem in D-S theory

Dempsters rule may not handle the conflicting belief structures in several situations. The Dempsters combination rule and its alternatives have been under the microscope. This paper focus on the conflicting belief structure problem and comes up with an evidence trap problem. A method of determining whether to select the Dempsters rule of combination or not when there is an evidence trap is proposed. The method deploys the conflict coefficient and the distance between belief structures as a two-dimensional measure with two thresholds settled. Numeric examples show the efficiency of the proposed method.